Agriculture sustains more than 58% of India's rural population, yet small and marginal farmers consistently lack access to timely, site-specific advisory services. Conventional extension systems are slow, language-restrictive, and inaccessible to low-literacy communities. This paper presents AgriNovaX — a voice-enabled agricultural intelligence platform that integrates Internet of Things (IoT) soil sensors, a Random Forest machine learning (ML) model, a GPT-based Natural Language Processing (NLP) reasoning module, and an offline text-to-speech (TTS) engine into a unified decision-support framework. The system collects real-time soil parameters (Nitrogen, Phosphorus, Potassium, pH, moisture) directly from IoT sensors connected via serial port, combines them with live weather API data (temperature, humidity, rainfall), and generates actionable recommendations on crop selection, fertilizer usage, and irrigation scheduling. The GPT reasoning module transforms ML predictions into farmer-readable natural language explanations delivered via pyttsx3 voice output in nine regional Indian languages — without requiring internet connectivity. The system was deployed as a desktop prototype (AgriNovaX Pro) and evaluated on the Kaggle Crop Recommendation Dataset (2,200 labelled samples, 22 crop categories), achieving a weighted F1 accuracy of 99.48% and an end-to-end response latency of 2-4 seconds. A soil health scoring dashboard, multilingual UI, and agronomic action plan further enhance usability. AgriNovaX bridges three critical gaps in existing systems: the absence of an integrated IoT-ML-NLP pipeline, dependence on internet connectivity for voice output, and insufficient multilingual accessibility — contributing to sustainable farming and reduced dependency on agricultural intermediaries.